Delete app.ipynb

app.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    }
-   ],
-   "source": [
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\angel\\workspace\\testpls\\.conda\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from PIL import Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_path = \"pokemons-model_transferlearning.keras\"\n",
-    "model = tf.keras.models.load_model(model_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the core prediction function\n",
-    "def predict_pokemons(image):\n",
-    "    # Preprocess image\n",
-    "    print(type(image))\n",
-    "    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image\n",
-    "    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale\n",
-    "    image = np.array(image)\n",
-    "    image = np.expand_dims(image, axis=0) # same as image[None, ...]\n",
-    "    \n",
-    "    # Predict\n",
-    "    prediction = model.predict(image)\n",
-    "    \n",
-    "    # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability\n",
-    "    # we could also change the output layer to dense(1, activation='sigmoid')\n",
-    "    #prediction = np.round(float(tf.sigmoid(prediction)), 2)\n",
-    "    #p_cat = (1 - prediction)\n",
-    "    #p_dog = prediction\n",
-    "    #return {'cat': p_cat, 'dog': p_dog}\n",
-    "\n",
-    "    # No need to apply sigmoid, as the output layer already uses softmax\n",
-    "    # Convert the probabilities to rounded values\n",
-    "    prediction = np.round(prediction, 2)\n",
-    " \n",
-    "    # Separate the probabilities for each class\n",
-    "    p_bulbasaur = prediction[0][0]  # Probability for class 'articuno'\n",
-    "    p_dratini = prediction[0][1]   # Probability for class 'moltres'\n",
-    "    p_gengar = prediction[0][2]    # Probability for class 'zapdos'\n",
-    " \n",
-    "  #  return {'charmander':  p_charmander, 'mewtwo': p_mewtwo, 'squirtle': p_squirtle}\n",
-    "    return {'Bulbasaur':  p_bulbasaur, 'Dratini': p_dratini, 'Gengar': p_gengar}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7864\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'>\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create the Gradio interface\n",
-    "input_image = gr.Image()\n",
-    "iface = gr.Interface(\n",
-    "    fn=predict_pokemons,\n",
-    "    inputs=input_image, \n",
-    "    outputs=gr.Label(),\n",
-    "    examples=[\"images/bulbasaur1.png\", \"images/bulbasaur2.png\", \"images/dratini1.png\", \"images/dratini2.png\", \"images/dratini3.png\", \"images/gengar1.png\", \"images/gengar2.png\", \"images/gengar3.png\"],   \n",
-    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
-    "iface.launch()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv_new",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}